Google Data Engineer Course & Curriculum

NCPL's hands-on Google Data Engineer training — full curriculum, real projects, certification prep, and job placement support. We train, mentor and place you.

Module 1: Introduction to GCP & Data Engineering

Master the basics of Google Cloud Platform and data engineering principles.

  • Cloud Computing Basics
  • GCP Services Overview
  • IAM and Resource Management
  • Networking Fundamentals

Module 2: Data Storage on GCP

Learn various GCP storage solutions and their practical applications.

  • Google Cloud Storage
  • BigQuery
  • Cloud SQL and Spanner
  • NoSQL Solutions

Module 3: Data Integration with GCP Tools

Explore tools for seamless data integration and migration.

  • Cloud Dataflow
  • Cloud Pub/Sub
  • Data Fusion
  • Cloud Functions

Module 4: Data Warehousing with BigQuery

Master BigQuery for enterprise-scale data warehousing.

  • Advanced Features
  • Partitioning and Clustering
  • Query Optimization
  • Data Loading and Exporting
  • Access and Cost Control

Module 5: Real-Time Data Streaming with GCP

Build real-time data processing systems using GCP tools.

  • Cloud Pub/Sub
  • Streaming Dataflow
  • Streaming Analytics
  • Cloud Functions

Module 6: Data Processing with Dataproc

Use Hadoop and Spark for large-scale data processing.

  • Intro to Dataproc
  • Spark on Dataproc
  • Cluster Configuration
  • Workflow Automation

Module 7: Data Analytics and Visualization

Create impactful data visualizations and dashboards.

  • Looker Studio
  • Advanced BigQuery Analytics
  • Collaboration and Sharing

Module 8: Data Governance and Security on GCP

Implement security and governance best practices.

  • IAM
  • Encryption
  • Monitoring
  • Best Practices

Module 9: Databricks on GCP

Learn Databricks for advanced analytics and ML workflows.

  • Databricks Introduction
  • Setup and Configuration
  • Data Engineering
  • GCS Integration
  • Advanced Analytics

Module 10: Airflow & PySpark

Build and orchestrate data pipelines with modern tools.

  • Apache Airflow
  • PySpark
  • Integration